Expected Allele Coverage and the Role of Mutation in Genetic Algorithms

It is part of the traditional lore of genetic algorithms that low mutation rates lead to efficient search of the solution space, while high mutation rates result in diffusion of search effort and premature extinction of favorable schemata in the population. We argue that the optimal mutation rate depends strongly on the choice of encoding, and that problems requiring nonbinary encodings may benefit from mutation rates much higher than those generally used with binary encodings. We introduce the notion of the expected allele coverage of a population, and discuss its role in guiding the choice of mutation rate and population size.